From caf1a9da7558f5afa3f32ba397e63a7d94c78b83 Mon Sep 17 00:00:00 2001 From: ~ Gius <70314219+teragramgius@users.noreply.github.com> Date: Sat, 2 Dec 2023 14:51:30 +0100 Subject: [PATCH] up new mashb --- 1.MASHUP/mashup(b).ipynb | 3164 -------------------------------------- 1 file changed, 3164 deletions(-) delete mode 100644 1.MASHUP/mashup(b).ipynb diff --git a/1.MASHUP/mashup(b).ipynb b/1.MASHUP/mashup(b).ipynb deleted file mode 100644 index 65e4bb8..0000000 --- a/1.MASHUP/mashup(b).ipynb +++ /dev/null @@ -1,3164 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "id": "NhhkAA5NnB50" - }, - "source": [ - "# unemployment and activity rate in Italy // foreign and Italian" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "wy8e7mV-nzAk" - }, - "source": [ - "# unemployment rate" - ] - }, - { - "cell_type": "code", - "execution_count": 151, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "NXypyWKpnZGF", - "outputId": "6a9894a9-3e89-419d-e4b1-f4d3a63c564e" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Requirement already satisfied: plotly in /usr/local/lib/python3.10/dist-packages (5.15.0)\n", - "Requirement already satisfied: tenacity>=6.2.0 in /usr/local/lib/python3.10/dist-packages (from plotly) (8.2.3)\n", - "Requirement already satisfied: packaging in /usr/local/lib/python3.10/dist-packages (from plotly) (23.2)\n", - "Requirement already satisfied: chart_studio in /usr/local/lib/python3.10/dist-packages (1.1.0)\n", - "Requirement already satisfied: plotly in /usr/local/lib/python3.10/dist-packages (from chart_studio) (5.15.0)\n", - "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from chart_studio) (2.31.0)\n", - "Requirement already satisfied: retrying>=1.3.3 in /usr/local/lib/python3.10/dist-packages (from chart_studio) (1.3.4)\n", - "Requirement already satisfied: six in /usr/local/lib/python3.10/dist-packages (from chart_studio) (1.16.0)\n", - "Requirement already satisfied: tenacity>=6.2.0 in /usr/local/lib/python3.10/dist-packages (from plotly->chart_studio) (8.2.3)\n", - 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"Requirement already satisfied: platformdirs>=2.5 in /usr/local/lib/python3.10/dist-packages (from jupyter-core>=4.6.0->jupyter-client->ipykernel->jupyter-dash) (4.0.0)\n" - ] - } - ], - "source": [ - "# per la chart\n", - "!pip install jupyter-dash\n", - "import dash_core_components as dcc\n", - "import dash_html_components as html\n", - "from dash.dependencies import Input, Output" - ] - }, - { - "cell_type": "code", - "execution_count": 153, - "metadata": { - "id": "-LYKXxTPnbcb" - }, - "outputs": [], - "source": [ - "# import packages\n", - "import pandas as pd\n", - "import numpy as np\n", - "import scipy as sp\n", - "import plotly.express as px\n", - "import chart_studio.plotly as py\n", - "import plotly.graph_objects as go" - ] - }, - { - "cell_type": "code", - "execution_count": 154, - "metadata": { - "id": "8CcpMDAoTfkE" - }, - "outputs": [], - "source": [ - "#first, a list with the more relevant names of the columns is createad\n", - "infocol = [\"Territory\", \"Data type\", \"Gender\", \"Highest level of education attained\", \"Citizenship\", \"TIME\", \"Value\"]\n", - "#then, the csv files are read and we use the list created before to only have information about those\n", - "\n", - "unem_r_Df = pd.read_csv('https://raw.githubusercontent.com/openaccesstoimmigrants/openaccesstoimmigrants/main/_datasets/ISTAT_Unemployment_rate_region_2018_2022_.csv')[infocol]" - ] - }, - { - "cell_type": "code", - "execution_count": 155, - "metadata": { - "id": "nCC0ygFjXAiU" - }, - "outputs": [], - "source": [ - "#here a function is defined in order to delete rows that might not interest us\n", - "def delete_row(dataframe, column_name, value_to_delete):\n", - " filtered_dataframe = dataframe[dataframe[column_name] != value_to_delete]\n", - "\n", - " return filtered_dataframe" - ] - }, - { - "cell_type": "code", - "execution_count": 156, - "metadata": { - "id": "qTADy5NiXGdx" - }, - "outputs": [], - "source": [ - "#sometimes the year value might include information about quarters, so this is another function to take only the values with 4 digits\n", - "def y_val(dataframe):\n", - "\n", - " dataframe['TIME'] = dataframe['TIME'].astype('str')\n", - " mask = (dataframe['TIME'].str.len() == 4)\n", - " dataframe= dataframe.loc[mask]\n", - "\n", - " return dataframe" - ] - }, - { - "cell_type": "code", - "execution_count": 157, - "metadata": { - "id": "fMxfF2GtXJ1M" - }, - "outputs": [], - "source": [ - "#applying the year function for the unem_r_Df\n", - "unem_r_Df = y_val(unem_r_Df)" - ] - }, - { - "cell_type": "code", - "execution_count": 158, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 424 - }, - "id": "Hrf4RXvBXRpd", - "outputId": "8d7cdc7c-98ba-463c-cd50-e9100b313b2a" - }, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - " Territory Data type Gender \\\n", - "150 Italy unemployment rate total \n", - "155 Italy unemployment rate total \n", - "160 Italy unemployment rate total \n", - "165 Italy unemployment rate total \n", - "166 Italy unemployment rate total \n", - "... ... ... ... \n", - "11497 Centro (I) unemployment rate total \n", - "11502 Centro (I) unemployment rate total \n", - "11507 Centro (I) unemployment rate total \n", - "11512 Centro (I) unemployment rate total \n", - "11513 Centro (I) unemployment rate total \n", - "\n", - " Highest level of education attained Citizenship TIME \\\n", - "150 upper and post secondary italian 2018 \n", - "155 upper and post secondary italian 2019 \n", - "160 upper and post secondary italian 2020 \n", - "165 upper and post secondary italian 2021 \n", - "166 upper and post secondary italian 2022 \n", - "... ... ... ... \n", - "11497 no educational degree, primary and lower secon... italian 2018 \n", - "11502 no educational degree, primary and lower secon... italian 2019 \n", - "11507 no educational degree, primary and lower secon... italian 2020 \n", - "11512 no educational degree, primary and lower secon... italian 2021 \n", - "11513 no educational degree, primary and lower secon... italian 2022 \n", - "\n", - " Value \n", - "150 9.715354 \n", - "155 8.966533 \n", - "160 8.425699 \n", - "165 8.673861 \n", - "166 7.368649 \n", - "... ... \n", - "11497 11.549672 \n", - "11502 9.998387 \n", - "11507 10.639449 \n", - "11512 11.358337 \n", - "11513 8.639894 \n", - "\n", - "[720 rows x 7 columns]" - ], - "text/html": [ - "\n", - "
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150Italyunemployment ratetotalupper and post secondaryitalian20189.715354
155Italyunemployment ratetotalupper and post secondaryitalian20198.966533
160Italyunemployment ratetotalupper and post secondaryitalian20208.425699
165Italyunemployment ratetotalupper and post secondaryitalian20218.673861
166Italyunemployment ratetotalupper and post secondaryitalian20227.368649
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11497Centro (I)unemployment ratetotalno educational degree, primary and lower secon...italian201811.549672
11502Centro (I)unemployment ratetotalno educational degree, primary and lower secon...italian20199.998387
11507Centro (I)unemployment ratetotalno educational degree, primary and lower secon...italian202010.639449
11512Centro (I)unemployment ratetotalno educational degree, primary and lower secon...italian202111.358337
11513Centro (I)unemployment ratetotalno educational degree, primary and lower secon...italian20228.639894
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\n" - ] - }, - "metadata": {}, - "execution_count": 158 - } - ], - "source": [ - "#applying the deletion function to take out rows we're not interested\n", - "unem_r_Df = delete_row(unem_r_Df, \"Gender\", \"females\")\n", - "unem_r_Df = delete_row(unem_r_Df, \"Gender\", \"males\")\n", - "unem_r_Df = delete_row(unem_r_Df, \"Citizenship\", \"total\")\n", - "unem_r_Df" - ] - }, - { - "cell_type": "code", - "execution_count": 159, - "metadata": { - "id": "0GHAR_-9YzER" - }, - "outputs": [], - "source": [ - "def filter_dataframe_by_value(df, column, value):\n", - " \"\"\"\n", - " Keep only the rows where the specified column has the given value.\n", - "\n", - " Parameters:\n", - " - df: pandas DataFrame\n", - " - column: str, column name\n", - " - value: value to filter on\n", - "\n", - " Returns:\n", - " - pandas DataFrame with filtered rows\n", - " \"\"\"\n", - " return df[df[column] == value]" - ] - }, - { - "cell_type": "code", - "execution_count": 160, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 990 - }, - "id": "xuE7WCVRY1OB", - "outputId": "3a8e4c15-683b-4bf7-92c6-89564cc5db8d" - }, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - " Territory Data type Gender Highest level of education attained \\\n", - "200 Italy unemployment rate total total \n", - "205 Italy unemployment rate total total \n", - "210 Italy unemployment rate total total \n", - "215 Italy unemployment rate total total \n", - "216 Italy unemployment rate total total \n", - "925 Italy unemployment rate total total \n", - "930 Italy unemployment rate total total \n", - "935 Italy unemployment rate total total \n", - "940 Italy unemployment rate total total \n", - "941 Italy unemployment rate total total \n", - "5424 Italy unemployment rate total total \n", - "5429 Italy unemployment rate total total \n", - "5434 Italy unemployment rate total total \n", - "5439 Italy unemployment rate total total \n", - "5440 Italy unemployment rate total total \n", - "9348 Italy unemployment rate total total \n", - "9353 Italy unemployment rate total total \n", - "9358 Italy unemployment rate total total \n", - "9363 Italy unemployment rate total total \n", - "9364 Italy unemployment rate total total \n", - "10623 Italy unemployment rate total total \n", - "10628 Italy unemployment rate total total \n", - "10633 Italy unemployment rate total total \n", - "10638 Italy unemployment rate total total \n", - "10639 Italy unemployment rate total total \n", - "11322 Italy unemployment rate total total \n", - "11327 Italy unemployment rate total total \n", - "11332 Italy unemployment rate total total \n", - "11337 Italy unemployment rate total total \n", - "11338 Italy unemployment rate total total \n", - "\n", - " Citizenship TIME Value \n", - "200 italian 2018 10.182574 \n", - "205 italian 2019 9.468407 \n", - "210 italian 2020 8.891429 \n", - "215 italian 2021 8.957717 \n", - "216 italian 2022 7.620690 \n", - "925 foreign 2018 13.973459 \n", - "930 foreign 2019 13.783589 \n", - "935 foreign 2020 13.315631 \n", - "940 foreign 2021 14.372673 \n", - "941 foreign 2022 11.999410 \n", - "5424 italian 2018 10.045196 \n", - "5429 italian 2019 9.346621 \n", - "5434 italian 2020 8.801839 \n", - "5439 italian 2021 8.832019 \n", - "5440 italian 2022 7.497267 \n", - "9348 foreign 2018 13.774335 \n", - "9353 foreign 2019 13.575160 \n", - "9358 foreign 2020 13.158501 \n", - "9363 foreign 2021 14.173818 \n", - "9364 foreign 2022 11.852616 \n", - "10623 italian 2018 10.373671 \n", - "10628 italian 2019 9.654127 \n", - "10633 italian 2020 9.078433 \n", - "10638 italian 2021 9.129100 \n", - "10639 italian 2022 7.762746 \n", - "11322 foreign 2018 14.075179 \n", - "11327 foreign 2019 13.883779 \n", - "11332 foreign 2020 13.420688 \n", - "11337 foreign 2021 14.520578 \n", - "11338 foreign 2022 12.035583 " - ], - "text/html": [ - "\n", - "
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TerritoryData typeGenderHighest level of education attainedCitizenshipTIMEValue
200Italyunemployment ratetotaltotalitalian201810.182574
205Italyunemployment ratetotaltotalitalian20199.468407
210Italyunemployment ratetotaltotalitalian20208.891429
215Italyunemployment ratetotaltotalitalian20218.957717
216Italyunemployment ratetotaltotalitalian20227.620690
925Italyunemployment ratetotaltotalforeign201813.973459
930Italyunemployment ratetotaltotalforeign201913.783589
935Italyunemployment ratetotaltotalforeign202013.315631
940Italyunemployment ratetotaltotalforeign202114.372673
941Italyunemployment ratetotaltotalforeign202211.999410
5424Italyunemployment ratetotaltotalitalian201810.045196
5429Italyunemployment ratetotaltotalitalian20199.346621
5434Italyunemployment ratetotaltotalitalian20208.801839
5439Italyunemployment ratetotaltotalitalian20218.832019
5440Italyunemployment ratetotaltotalitalian20227.497267
9348Italyunemployment ratetotaltotalforeign201813.774335
9353Italyunemployment ratetotaltotalforeign201913.575160
9358Italyunemployment ratetotaltotalforeign202013.158501
9363Italyunemployment ratetotaltotalforeign202114.173818
9364Italyunemployment ratetotaltotalforeign202211.852616
10623Italyunemployment ratetotaltotalitalian201810.373671
10628Italyunemployment ratetotaltotalitalian20199.654127
10633Italyunemployment ratetotaltotalitalian20209.078433
10638Italyunemployment ratetotaltotalitalian20219.129100
10639Italyunemployment ratetotaltotalitalian20227.762746
11322Italyunemployment ratetotaltotalforeign201814.075179
11327Italyunemployment ratetotaltotalforeign201913.883779
11332Italyunemployment ratetotaltotalforeign202013.420688
11337Italyunemployment ratetotaltotalforeign202114.520578
11338Italyunemployment ratetotaltotalforeign202212.035583
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TerritoryData typeCitizenshipTIMEValue
200Italyunemployment rateitalian201810.182574
205Italyunemployment rateitalian20199.468407
210Italyunemployment rateitalian20208.891429
215Italyunemployment rateitalian20218.957717
216Italyunemployment rateitalian20227.620690
925Italyunemployment rateforeign201813.973459
930Italyunemployment rateforeign201913.783589
935Italyunemployment rateforeign202013.315631
940Italyunemployment rateforeign202114.372673
941Italyunemployment rateforeign202211.999410
5424Italyunemployment rateitalian201810.045196
5429Italyunemployment rateitalian20199.346621
5434Italyunemployment rateitalian20208.801839
5439Italyunemployment rateitalian20218.832019
5440Italyunemployment rateitalian20227.497267
9348Italyunemployment rateforeign201813.774335
9353Italyunemployment rateforeign201913.575160
9358Italyunemployment rateforeign202013.158501
9363Italyunemployment rateforeign202114.173818
9364Italyunemployment rateforeign202211.852616
10623Italyunemployment rateitalian201810.373671
10628Italyunemployment rateitalian20199.654127
10633Italyunemployment rateitalian20209.078433
10638Italyunemployment rateitalian20219.129100
10639Italyunemployment rateitalian20227.762746
11322Italyunemployment rateforeign201814.075179
11327Italyunemployment rateforeign201913.883779
11332Italyunemployment rateforeign202013.420688
11337Italyunemployment rateforeign202114.520578
11338Italyunemployment rateforeign202212.035583
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- }, - "metadata": {} - } - ], - "source": [ - "from matplotlib import pyplot as plt\n", - "import seaborn as sns\n", - "def _plot_series(series, series_name, series_index=0):\n", - " from matplotlib import pyplot as plt\n", - " import seaborn as sns\n", - " palette = list(sns.palettes.mpl_palette('Dark2'))\n", - " xs = series['TIME']\n", - " ys = series['Value']\n", - "\n", - " plt.plot(xs, ys, label=series_name, color=palette[series_index % len(palette)])\n", - "\n", - "fig, ax = plt.subplots(figsize=(10, 5.2), layout='constrained')\n", - "df_sorted = filtered_dfDEF.sort_values('TIME', ascending=True)\n", - "for i, (series_name, series) in enumerate(df_sorted.groupby('Citizenship')):\n", - " _plot_series(series, series_name, i)\n", - " fig.legend(title='Citizenship', bbox_to_anchor=(1, 1), loc='upper left')\n", - "sns.despine(fig=fig, ax=ax)\n", - "plt.xlabel('TIME')\n", - "_ = plt.ylabel('Value')" - ] - }, - { - "cell_type": "code", - "execution_count": 163, - "metadata": { - "id": "k7w1f1iBfpoG" - }, - "outputs": [], - "source": [ - "for18_filtered_dfDEF = filtered_dfDEF[(filtered_dfDEF['TIME'] == '2018') & (filtered_dfDEF['Citizenship'] == 'foreign')]\n", - "for19_filtered_dfDEF = filtered_dfDEF[(filtered_dfDEF['TIME'] == '2019') & (filtered_dfDEF['Citizenship'] == 'foreign')]\n", - "for20_filtered_dfDEF = filtered_dfDEF[(filtered_dfDEF['TIME'] == '2020') & (filtered_dfDEF['Citizenship'] == 'foreign')]\n", - "for21_filtered_dfDEF = filtered_dfDEF[(filtered_dfDEF['TIME'] == '2021') & (filtered_dfDEF['Citizenship'] == 'foreign')]\n", - "for22_filtered_dfDEF = filtered_dfDEF[(filtered_dfDEF['TIME'] == '2022') & (filtered_dfDEF['Citizenship'] == 'foreign')]" - ] - }, - { - "cell_type": "code", - "execution_count": 164, - "metadata": { - "id": "i0My1_J7gdsL" - }, - "outputs": [], - "source": [ - "unemFor18 = for18_filtered_dfDEF['Value'].sum()\n", - "unemFor19 = for19_filtered_dfDEF['Value'].sum()\n", - "unemFor20 = for20_filtered_dfDEF['Value'].sum()\n", - "unemFor21 = for21_filtered_dfDEF['Value'].sum()\n", - "unemFor22 = for22_filtered_dfDEF['Value'].sum()\n", - "\n", - "unFo = [unemFor18, unemFor19, unemFor20, unemFor21, unemFor22]" - ] - }, - { - "cell_type": "code", - "execution_count": 165, - "metadata": { - "id": "C5B5NA70gd1C" - }, - "outputs": [], - "source": [ - "it18_filtered_dfDEF = filtered_dfDEF[(filtered_dfDEF['TIME'] == '2018') & (filtered_dfDEF['Citizenship'] == 'italian')]\n", - "it19_filtered_dfDEF = filtered_dfDEF[(filtered_dfDEF['TIME'] == '2019') & (filtered_dfDEF['Citizenship'] == 'italian')]\n", - "it20_filtered_dfDEF = filtered_dfDEF[(filtered_dfDEF['TIME'] == '2020') & (filtered_dfDEF['Citizenship'] == 'italian')]\n", - "it21_filtered_dfDEF = filtered_dfDEF[(filtered_dfDEF['TIME'] == '2021') & (filtered_dfDEF['Citizenship'] == 'italian')]\n", - "it22_filtered_dfDEF = filtered_dfDEF[(filtered_dfDEF['TIME'] == '2022') & (filtered_dfDEF['Citizenship'] == 'italian')]" - ] - }, - { - "cell_type": "code", - "execution_count": 166, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "3OIDp6SShJOT", - "outputId": "412fcbf6-a291-4dd2-d1a2-d55f2e654438" - }, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "[30.601441, 28.469155, 26.771701, 26.918836, 22.880703]" - ] - }, - "metadata": {}, - "execution_count": 166 - } - ], - "source": [ - "unemIt18 = it18_filtered_dfDEF['Value'].sum()\n", - "unemIt19 = it19_filtered_dfDEF['Value'].sum()\n", - "unemIt20 = it20_filtered_dfDEF['Value'].sum()\n", - "unemIt21 = it21_filtered_dfDEF['Value'].sum()\n", - "unemIt22 = it22_filtered_dfDEF['Value'].sum()\n", - "\n", - "unIt = [unemIt18, unemIt19, unemIt20, unemIt21, unemIt22]\n", - "unIt" - ] - }, - { - "cell_type": "code", - "execution_count": 167, - "metadata": { - "id": "Ljwalm5mjFfd" - }, - "outputs": [], - "source": [ - "year = ['2018', '2019', '2020', '2021', '2022', '2018', '2019', '2020', '2021', '2022']\n", - "citizenship = ['foreign', 'foreign', 'foreign', 'foreign', 'foreign','italian', 'italian', 'italian', 'italian', 'italian']\n", - "unemploymentRates = unFo + unIt" - ] - }, - { - "cell_type": "code", - "execution_count": 168, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 363 - }, - "id": "qZI2EzF3cgNC", - "outputId": "6e24b47d-f7f9-4ad6-816a-2c3f9e2f30a4" - }, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - " Citizenship TIME Value\n", - "0 foreign 2018 41.822973\n", - "1 foreign 2019 41.242528\n", - "2 foreign 2020 39.894820\n", - "3 foreign 2021 43.067069\n", - "4 foreign 2022 35.887609\n", - "5 italian 2018 30.601441\n", - "6 italian 2019 28.469155\n", - "7 italian 2020 26.771701\n", - "8 italian 2021 26.918836\n", - "9 italian 2022 22.880703" - ], - "text/html": [ - "\n", - "
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CitizenshipTIMEValue
0foreign201841.822973
1foreign201941.242528
2foreign202039.894820
3foreign202143.067069
4foreign202235.887609
5italian201830.601441
6italian201928.469155
7italian202026.771701
8italian202126.918836
9italian202222.880703
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CitizenshipYear% UnemploymentGDP
0foreign201841.8229730.925811
1foreign201941.2425280.483198
2foreign202039.894820-8.974192
3foreign202143.0670698.313760
4foreign202235.8876093.724549
5italian201830.6014410.925811
6italian201928.4691550.483198
7italian202026.771701-8.974192
8italian202126.9188368.313760
9italian202222.8807033.724549
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\n" - }, - "metadata": {} - } - ], - "source": [ - "import seaborn as sns\n", - "import matplotlib.pyplot as plt\n", - "\n", - "# Assuming df contains columns: 'Year', '% Unemployment', 'GDP Growth Rate', 'Citizenship'\n", - "\n", - "# Filter out NaN values\n", - "df_filtered = df.dropna(subset=['% Unemployment', 'Change in GDP'])\n", - "\n", - "# Scatterplot with regression line\n", - "sns.lmplot(x='% Unemployment', y='Change in GDP', hue='Year', data=df_filtered, markers='o',\n", - " scatter_kws={'s': 100}, palette='viridis', height=6, aspect=1.5)\n", - "\n", - "# Set plot labels and title\n", - "plt.xlabel('Unemployment Rate (%)')\n", - "plt.ylabel('GDP Growth Rate')\n", - "plt.title('Okun\\'s Law: Unemployment Rate vs. Change in GDP by Year')\n", - "\n", - "# Show the plot\n", - "plt.show()\n" - ] - }, - { - "cell_type": "code", - "execution_count": 172, - "metadata": { - "id": "Dmxrwy7Slclj" - }, - "outputs": [], - "source": [ - "df[\"Year\"] = df[\"Year\"].astype(int)" - ] - }, - { - "cell_type": "code", - "execution_count": 173, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 495 - }, - "id": "0FwxL-8kNMTh", - "outputId": "c4c1ff0a-9d15-4cbb-a83a-9b8f3bd1ce5a" - }, - "outputs": [ - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
" - ], - "image/png": 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- }, - "metadata": {} - } - ], - "source": [ - "desired_years = [2018, 2019, 2020, 2021, 2022]\n", - "\n", - "\n", - "citizenship_groups = df.groupby('Citizenship')\n", - "# Plotting\n", - "fig, ax1 = plt.subplots()\n", - "\n", - "# Plot the Unemployment Rate for each citizenship group\n", - "for group_name, group_df in citizenship_groups:\n", - " ax1.plot(group_df['Year'], group_df['% Unemployment'], marker='o', label=f'Unemployment Rate ({group_name})')\n", - "\n", - "ax1.set_xlabel('Year')\n", - "ax1.set_ylabel('Unemployment Rate (%)', color='tab:blue')\n", - "ax1.tick_params(axis='y', labelcolor='tab:blue')\n", - "\n", - "# Create a secondary y-axis for GDP\n", - "ax2 = ax1.twinx()\n", - "ax2.set_ylabel('GDP', color='tab:green')\n", - "\n", - "# Plot the GDP for each citizenship group\n", - "for group_name, group_df in citizenship_groups:\n", - " ax2.plot(group_df['Year'], group_df['GDP'], marker='s', label=f'GDP ({group_name})')\n", - "\n", - "ax2.tick_params(axis='y', labelcolor='tab:green')\n", - "\n", - "# Display Okun's Law on the same chart for each citizenship group\n", - "for group_name, group_df in citizenship_groups:\n", - " ax2.plot(group_df['Year'], group_df[\"Okun's Law\"], linestyle='--', label=f\"Okun's Law ({group_name})\")\n", - "\n", - "\n", - "plt.xticks(desired_years)\n", - "# Display the legend\n", - "fig.tight_layout()\n", - "fig.legend(loc='upper left', bbox_to_anchor=(0.7, 1.0))\n", - "\n", - "# Display the chart\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 174, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 542 - }, - "id": "PmdFaa1ZrEbD", - "outputId": "eee9710c-d1f2-4267-f3e8-e8024add1772" - }, - "outputs": [ - { - "output_type": "display_data", - "data": { - "text/html": [ - "\n", - "\n", - "\n", - "
\n", - "
\n", - "\n", - "" - ] - }, - "metadata": {} - } - ], - "source": [ - "# Set the desired fixed years\n", - "desired_years = [2018, 2019, 2020, 2021, 2022]\n", - "\n", - "# Group by Citizenship\n", - "citizenship_groups = df.groupby('Citizenship')\n", - "\n", - "# Create traces for Unemployment Rate, GDP, and Okun's Law\n", - "traces = []\n", - "\n", - "for group_name, group_df in citizenship_groups:\n", - " # Unemployment Rate trace\n", - " trace_unemployment = go.Scatter(x=group_df['Year'], y=group_df['% Unemployment'],\n", - " mode='markers+lines', name=f'Unemployment Rate ({group_name})',\n", - " line=dict(color='blue'), marker=dict(symbol='circle', size=8))\n", - "\n", - " # Okun's Law trace\n", - " trace_okun = go.Scatter(x=group_df['Year'], y=group_df[\"Okun's Law\"],\n", - " mode='markers+lines', name=f\"Okun's Law ({group_name})\",\n", - " line=dict(color='red', dash='dash'), marker=dict(symbol='diamond', size=8))\n", - "\n", - " traces.extend([trace_unemployment, trace_okun])\n", - "\n", - "# Separate trace for GDP\n", - "trace_gdp = go.Scatter(x=df['Year'], y=df['GDP'],\n", - " mode='markers+lines', name='GDP',\n", - " line=dict(color='green'), marker=dict(symbol='square', size=8))\n", - "\n", - "traces.append(trace_gdp)\n", - "\n", - "# Create layout\n", - "layout = go.Layout(title='Unemployment Rate, GDP, and Okun\\'s Coefficient Over Years',\n", - " xaxis=dict(title='Year', tickmode='array', tickvals=desired_years),\n", - " yaxis=dict(title='Percentage'))\n", - "\n", - "# Create figure\n", - "fig = go.Figure(data=traces, layout=layout)\n", - "\n", - "# Display the chart\n", - "fig.show()\n" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "T4GsA7lfn3rW" - }, - "source": [ - "# activity rate (to finish)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "oleq8mK4oC21" - }, - "outputs": [], - "source": [ - "#first, a list with the more relevant names of the columns is createad\n", - "infocol = [\"Territory\", \"Data type\", \"Gender\", \"Highest level of education attained\", \"Citizenship\", \"TIME\", \"Value\"]\n", - "#then, the csv files are read and we use the list created before to only have information about those\n", - "\n", - "act_r_Df = pd.read_csv('https://raw.githubusercontent.com/openaccesstoimmigrants/openaccesstoimmigrants/main/_datasets/ISTAT_Activity_Rate_Region_2018-2022.csv')[infocol]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "8k9gPSAAoC23" - }, - "outputs": [], - "source": [ - "#here a function is defined in order to delete rows that might not interest us\n", - "def delete_row(dataframe, column_name, value_to_delete):\n", - " filtered_dataframe = dataframe[dataframe[column_name] != value_to_delete]\n", - "\n", - " return filtered_dataframe" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "e2fYLVVGoC23" - }, - "outputs": [], - "source": [ - "#sometimes the year value might include information about quarters, so this is another function to take only the values with 4 digits\n", - "def y_val(dataframe):\n", - "\n", - " dataframe['TIME'] = dataframe['TIME'].astype('str')\n", - " mask = (dataframe['TIME'].str.len() == 4)\n", - " dataframe= dataframe.loc[mask]\n", - "\n", - " return dataframe" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "Rn9E2pE7oC24" - }, - "outputs": [], - "source": [ - "#applying the year function for the unem_r_Df\n", - "act_r_Df = y_val(act_r_Df)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "Kyc4lSyroC24" - }, - "outputs": [], - "source": [ - "#applying the deletion function to take out rows we're not interested\n", - "act_r_Df = delete_row(act_r_Df, \"Gender\", \"females\")\n", - "act_r_Df = delete_row(act_r_Df, \"Gender\", \"males\")\n", - "act_r_Df = delete_row(act_r_Df, \"Citizenship\", \"total\")\n", - "act_r_Df" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "V63ZG0JEoC25" - }, - "outputs": [], - "source": [ - "def filter_dataframe_by_value(df, column, value):\n", - " \"\"\"\n", - " Keep only the rows where the specified column has the given value.\n", - "\n", - " Parameters:\n", - " - df: pandas DataFrame\n", - " - column: str, column name\n", - " - value: value to filter on\n", - "\n", - " Returns:\n", - " - pandas DataFrame with filtered rows\n", - " \"\"\"\n", - " return df[df[column] == value]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "dgBft3VeoC25" - }, - "outputs": [], - "source": [ - "filtered_df = filter_dataframe_by_value(act_r_Df, 'Territory', 'Italy')\n", - "filtered_df\n", - "filtered_dfDEF = filter_dataframe_by_value(filtered_df, 'Highest level of education attained', 'total')\n", - "filtered_dfDEF" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "5s_KDUAHoC26" - }, - "outputs": [], - "source": [ - "filtered_dfDEF = filtered_dfDEF.drop('Gender', axis=1)\n", - "filtered_dfDEF = filtered_dfDEF.drop('Highest level of education attained', axis=1)\n", - "filtered_dfDEF" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "XGiKDBlDoC26" - }, - "outputs": [], - "source": [ - "from matplotlib import pyplot as plt\n", - "import seaborn as sns\n", - "def _plot_series(series, series_name, series_index=0):\n", - " from matplotlib import pyplot as plt\n", - " import seaborn as sns\n", - " palette = list(sns.palettes.mpl_palette('Dark2'))\n", - " xs = series['TIME']\n", - " ys = series['Value']\n", - "\n", - " plt.plot(xs, ys, label=series_name, color=palette[series_index % len(palette)])\n", - "\n", - "fig, ax = plt.subplots(figsize=(10, 5.2), layout='constrained')\n", - "df_sorted = filtered_dfDEF.sort_values('TIME', ascending=True)\n", - "for i, (series_name, series) in enumerate(df_sorted.groupby('Citizenship')):\n", - " _plot_series(series, series_name, i)\n", - " fig.legend(title='Citizenship', bbox_to_anchor=(1, 1), loc='upper left')\n", - "sns.despine(fig=fig, ax=ax)\n", - "plt.xlabel('TIME')\n", - "_ = plt.ylabel('Value')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "zYA6Wn_QoC28" - }, - "outputs": [], - "source": [ - "for18_filtered_dfDEF = filtered_dfDEF[(filtered_dfDEF['TIME'] == '2018') & (filtered_dfDEF['Citizenship'] == 'foreign')]\n", - "for19_filtered_dfDEF = filtered_dfDEF[(filtered_dfDEF['TIME'] == '2019') & (filtered_dfDEF['Citizenship'] == 'foreign')]\n", - "for20_filtered_dfDEF = filtered_dfDEF[(filtered_dfDEF['TIME'] == '2020') & (filtered_dfDEF['Citizenship'] == 'foreign')]\n", - "for21_filtered_dfDEF = filtered_dfDEF[(filtered_dfDEF['TIME'] == '2021') & (filtered_dfDEF['Citizenship'] == 'foreign')]\n", - "for22_filtered_dfDEF = filtered_dfDEF[(filtered_dfDEF['TIME'] == '2022') & (filtered_dfDEF['Citizenship'] == 'foreign')]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "iFHPm-SVoC28" - }, - "outputs": [], - "source": [ - "unemFor18 = for18_filtered_dfDEF['Value'].sum()\n", - "unemFor19 = for19_filtered_dfDEF['Value'].sum()\n", - "unemFor20 = for20_filtered_dfDEF['Value'].sum()\n", - "unemFor21 = for21_filtered_dfDEF['Value'].sum()\n", - "unemFor22 = for22_filtered_dfDEF['Value'].sum()\n", - "\n", - "unFo = [unemFor18, unemFor19, unemFor20, unemFor21, unemFor22]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "oSTrqF7loC29" - }, - "outputs": [], - "source": [ - "it18_filtered_dfDEF = filtered_dfDEF[(filtered_dfDEF['TIME'] == '2018') & (filtered_dfDEF['Citizenship'] == 'italian')]\n", - "it19_filtered_dfDEF = filtered_dfDEF[(filtered_dfDEF['TIME'] == '2019') & (filtered_dfDEF['Citizenship'] == 'italian')]\n", - "it20_filtered_dfDEF = filtered_dfDEF[(filtered_dfDEF['TIME'] == '2020') & (filtered_dfDEF['Citizenship'] == 'italian')]\n", - "it21_filtered_dfDEF = filtered_dfDEF[(filtered_dfDEF['TIME'] == '2021') & (filtered_dfDEF['Citizenship'] == 'italian')]\n", - "it22_filtered_dfDEF = filtered_dfDEF[(filtered_dfDEF['TIME'] == '2022') & (filtered_dfDEF['Citizenship'] == 'italian')]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "dM_71mwdoC29" - }, - "outputs": [], - "source": [ - "unemIt18 = it18_filtered_dfDEF['Value'].sum()\n", - "unemIt19 = it19_filtered_dfDEF['Value'].sum()\n", - "unemIt20 = it20_filtered_dfDEF['Value'].sum()\n", - "unemIt21 = it21_filtered_dfDEF['Value'].sum()\n", - "unemIt22 = it22_filtered_dfDEF['Value'].sum()\n", - "\n", - "unIt = [unemIt18, unemIt19, unemIt20, unemIt21, unemIt22]\n", - "unIt" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "NNPM8oMpoC29" - }, - "outputs": [], - "source": [ - "year = ['2018', '2019', '2020', '2021', '2022', '2018', '2019', '2020', '2021', '2022']\n", - "citizenship = ['foreign', 'foreign', 'foreign', 'foreign', 'foreign','italian', 'italian', 'italian', 'italian', 'italian']\n", - "unemploymentRates = unFo + unIt" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "rsrdwwBzoC29" - }, - "outputs": [], - "source": [ - "df = pd.DataFrame({'Citizenship': citizenship, 'TIME': year, 'Value': unemploymentRates})\n", - "df" - ] - } - ], - "metadata": { - "colab": { - "provenance": [], - "toc_visible": true - }, - "kernelspec": { - "display_name": "Python 3", - "name": "python3" - }, - "language_info": { - "name": "python" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} \ No newline at end of file